{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 7  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_gpt_zero'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_121444-83bd2oxn</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn' target=\"_blank\">finetune_chetGPT_hatespeech_gpt_zero</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x16fdede77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_7__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_7__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_7__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_7__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 879.4873096446701, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.8908629441624365, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346518 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1732590 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 880.632, 868.0\n",
      "p5 / p95: 853.0, 928.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.752, 4.5\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220158 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1100790 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 566676\n",
      "\n",
      "#### Estimated cost: 4.53 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-Yx6FS1ARJFjyldM2gvpV6QLZ\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-7-t-1-trn-250:B5sqNERS has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.02, validation loss=0.00\n",
      "Step 49/63: training loss=0.00, validation loss=0.00\n",
      "Step 50/63: training loss=0.00, validation loss=0.12\n",
      "Step 51/63: training loss=0.03, validation loss=0.09\n",
      "Step 52/63: training loss=0.00, validation loss=0.20, full validation loss=0.12\n",
      "Step 53/63: training loss=0.00, validation loss=0.03\n",
      "Step 54/63: training loss=0.00, validation loss=0.14\n",
      "Step 55/63: training loss=0.03, validation loss=0.08\n",
      "Step 56/63: training loss=0.01, validation loss=0.10\n",
      "Step 57/63: training loss=0.07, validation loss=0.30\n",
      "Step 58/63: training loss=0.00, validation loss=0.30\n",
      "Step 59/63: training loss=0.00, validation loss=0.13\n",
      "Step 60/63: training loss=0.03, validation loss=0.10\n",
      "Step 61/63: training loss=0.00, validation loss=0.16\n",
      "Step 62/63: training loss=0.00, validation loss=0.00\n",
      "Step 63/63: training loss=0.00, validation loss=0.09, full validation loss=0.09\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:15<00:00,  2.01it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.9430894308943089,\n",
      "                 'precision': 0.925531914893617,\n",
      "                 'recall': 0.9613259668508287,\n",
      "                 'support': 181},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.9389312977099237,\n",
      "                       'precision': 0.9919354838709677,\n",
      "                       'recall': 0.8913043478260869,\n",
      "                       'support': 138},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.8280254777070064,\n",
      "                  'precision': 0.7926829268292683,\n",
      "                  'recall': 0.8666666666666667,\n",
      "                  'support': 75},\n",
      " 'accuracy': 0.9187817258883249,\n",
      " 'macro avg': {'f1-score': 0.9033487354370796,\n",
      "               'precision': 0.9033834418646176,\n",
      "               'recall': 0.9064323271145275,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.919729992141789,\n",
      "                  'precision': 0.9235015047769881,\n",
      "                  'recall': 0.9187817258883249,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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THpNc+1n/xBNPqEiRIhk+66tVq6Zy5cpl2lbr1q1tHlesWFGSrDP1ry4/ffq0TTrxtm3b1LZtW/n7+8vR0VHOzs7q0qWLUlJStHfv3hs/2SzMmzdPQ4cO1SuvvKJevXpZy3/66Sc1atRIISEhSk5Oth4tWrSQJK1Zs0ZSWrzq5eWVIbbs1KnTTfcpK7kVd13PHXfcoaJFi2rYsGH66KOPsr1OX27EwuHh4WrYsKFmzpypU6dO6YcfftDTTz+dZX2zfubatm0rZ2fnbNVt166dnn32WQ0ZMoRsE+QpDAwCMEVAQIA8PDx08ODBbNU/deqUpMzTGUJCQqznb4a/v7/N4/QUmJvdHKRUqVKSZNdzy+p5pZ+/mtn9zczw4cP1zjvv6Pfff1eLFi3k7++vxo0ba/PmzVleY+975OHhITc3N5syV1dXXb582a6+3n///YqIiND06dP1+eef6+mnn85yDciBAwfq1Vdf1cMPP6wff/xRf/zxhzZt2qTq1avb9fp5eHjYtWPdHXfcofvuu0+XL1/Wk08+mWs7OQMAcD0dOnTQnXfeqREjRmS6Xt+pU6fk5OSUYYkSi8Wi4ODgDJ/11/u88/Pzs3ns4uJy3fL0+CAyMlL33Xefjh49qvfee0/r1q3Tpk2b9OGHH0q6+Xho1apV6tatm7p06aLXX3/d5tzx48f1448/ytnZ2eaoXLmyJOnkyZOS0l6foKCgDG0HBwdnqw+lSpXKsdjYrLjrenx8fLRmzRrVqFFDL7/8sipXrqyQkBCNGjXquus/5lYs3KNHD/3444+aOHGi3N3d9fjjj2daz8yfOXtjwKefflpJSUlycnJS//797boWyCkMDAIwhaOjoxo3bqwtW7Zk2DwkM+kBQHR0dIZzx44ds1lf0M3NLdMNLNKDtpzWqFEjOTs76/vvv89WfX9//yyfl6SbXjsxM9l9bZycnDRw4EBt3bpVp0+f1rx58xQVFaXmzZvr4sWLmbZtz3tktu7du2vatGk6ffq0unbtmmW9L774Ql26dNG4cePUvHlz1a1bV7Vr17b7Z+N6m89k5tNPP9XixYtVt25dTZkyRX/88Ydd1wMAcDtYLBa99dZb2r9/vz7++OMM5/39/ZWcnKwTJ07YlBuGoZiYmAyf9fZ+XmbH999/r/j4eC1cuFCdO3fWvffeq9q1a1sHEG/G33//rYcfflgNGjTQJ598kuF8QECAmjVrpk2bNmV69OjRQ1La63P8+PEM12d385HmzZvr+PHj2dpI43bGXekDitfGkJnFT1WrVtX8+fN16tQpbd++Xe3bt9eYMWP07rvvZtn+7YyFr/boo4/Kw8NDb775pjp06JDljtlm/szZ828iPj5eTz31lMqVKyd3d3f17NnT7vsBOYGBQQCmGT58uAzDUK9evZSYmJjhfFJSkn788UdJsi6C/cUXX9jU2bRpk/755x/rrrBS2k5nf//9t029vXv32uw0bC9XV9dsfxsYHBysnj17atmyZfrss88yrbN//35rHxs3bqzdu3dr69atNnU+++wzWSwWNWrU6Kb7fa2beW18fX31+OOPq1+/fjp9+nSGHenS3XPPPXJ3d8/wHh05ckQrV660eY/M1rVrV7Vp00ZDhgxRiRIlsqxnsVgy7IC9ePFiHT161KbMzFmYO3bsUP/+/dWlSxetW7dO1apVU/v27XXmzJlbbhsAALM1adJETZs21ZgxYzLsBpz+WX7tZ/23336r+Pj4HP2sT5c+sHL157lhGJkO6GVHZGSkWrRooTJlyujbb7/NNM2zdevW2rlzp8qWLavatWtnONJntjVq1Ejnz5/XokWLbK6fO3dutvry4osvqkiRIurbt6/NbrTpDMOwbmpxO+OusLAwScoQQ177PK9msVhUvXp1TZo0Sb6+vhni3Kvdzlj4au7u7ho5cqTatGlj3cQuM/b8zNnzN8ON9OnTR5GRkVq4cKFmzJihRYsWadKkSaa0DdwKdiUGYJp77rlH06ZNU9++fVWrVi09++yzqly5spKSkrRt2zZ9/PHHqlKlitq0aaPy5cvrmWee0QcffCAHBwe1aNHCuitxaGioXnzxRWu7Tz31lDp37qy+ffvqscce0+HDhzVhwgS7d+a9WtWqVbVw4UJNmzZNtWrVkoODg2rXrp1l/YkTJ+rAgQPq1q2bli1bpkceeURBQUE6efKkVqxYoVmzZmn+/PmqVq2aXnzxRX322Wdq1aqVxowZo9KlS2vx4sWaOnWqnn322SzX5rkZ2X1t2rRpoypVqqh27doqVqyYDh8+rMmTJ6t06dKZ7p4mpQ0gvvrqq3r55ZfVpUsXdezYUadOndJrr70mNzc3jRo1yrTnca2QkJBszdBs3bq1Zs+erQoVKqhatWrasmWL3n77bZsdhyWpbNmycnd315dffqmKFSvK09NTISEh1sA/u+Lj49WuXTuFh4dr6tSpcnFx0VdffaU777xT3bt3z/asUgAAbqe33npLtWrVUmxsrDVdVpKaNm2q5s2ba9iwYYqLi1P9+vWtuxLXrFlTTz31VI73rWnTpnJxcVHHjh01dOhQXb58WdOmTbvpL9xatGihs2fPasqUKdq1a5fNubJly6pYsWIaM2aMVqxYoXr16ql///4qX768Ll++rEOHDmnJkiX66KOPVLJkSXXp0kWTJk1Sly5d9MYbbygiIkJLlizRsmXLstWX8PBwzZ8/X+3bt1eNGjX03HPPqWbNmpKk3bt3a+bMmTIMQ4888shtjbvq1Kmj8uXLa/DgwUpOTlbRokX13Xffaf369Tb1fvrpJ02dOlUPP/ywypQpI8MwtHDhQp09e/a6a+Pdzlj4WgMHDtTAgQOvW8eenzl7/2bIyqeffqovvvhCs2bNUuXKlVW5cmU999xzGjZsmOrXr59hPUbgtsqtXU8AFFzbt283unbtapQqVcpwcXExihQpYtSsWdMYOXKkERsba62XkpJivPXWW0a5cuUMZ2dnIyAgwOjcubMRFRVl015qaqoxYcIEo0yZMoabm5tRu3ZtY+XKlVnuSvz111/bXJ/ZzmCnT582Hn/8ccPX19ewWCxGdn4dJicnG3PmzDEeeOABw8/Pz3BycjKKFStmtGjRwpg7d66RkpJirXv48GGjU6dOhr+/v+Hs7GyUL1/eePvtt23qpPfr7bffznAvScaoUaOsj7PalTi7r827775r1KtXzwgICDBcXFyMUqVKGT169DAOHTqU4R7X7lD36aefGtWqVTNcXFwMHx8f46GHHrLu1peua9euRpEiRTI8j1GjRmXrtb12l8TMZLaz8JkzZ4wePXoYgYGBhoeHh3Hvvfca69aty/D8DcMw5s2bZ1SoUMFwdna2eX2z6nv6uat3ouvcubPh4eGR4fl//fXXhiRj0qRJN3yuAADklKziBcMwjE6dOhmSMnzeXrp0yRg2bJhRunRpw9nZ2ShevLjx7LPPGmfOnLGpl9nutoaRdfyVVV/SY4MTJ05Yy3788UejevXqhpubm1GiRAljyJAhxs8//2yzQ65hZG9XYklZHlfHECdOnDD69+9vhIeHG87Ozoafn59Rq1YtY8SIEcaFCxes9Y4cOWI89thjhqenp+Hl5WU89thjxoYNG7K1K3G6/fv3G3379jXuuOMOw9XV1XB3dzcqVapkDBw4MMfjrqzet7179xrNmjUzvL29jWLFihnPP/+8sXjxYpvX/N9//zU6duxolC1b1nB3dzd8fHyMunXrGrNnz85wj2t3hjY7Fs5MVj9718psZ+Hs/sxl9TfD9fp+7d8ef//9t+Hu7p7hNbp8+bJRq1YtIywsLMO/N+B2shiGYeT04CMAAAAAAACAvIU1BgEAAAAAAIBCiIFBAAAAAAAAoBBiYBAAAAAAAAAohBgYBAAAAAAAAAohBgYBAAAAAACAQoiBQQAAAAAAAKAQcsrtDgBmSU1N1bFjx+Tl5SWLxZLb3QEA3EaGYej8+fMKCQmRg0POfu95+fJlJSYmmtaei4uL3NzcTGsPKIiI8wCg8LqdcZ5kbqyXH+I8BgZRYBw7dkyhoaG53Q0AQC6KiopSyZIlc6z9y5cvK7y0p2JiU0xrMzg4WAcPHszzQSOQm4jzAAA5HedJ5sd6+SHOY2AQBYaXl5ck6fDWMHl7kiVfGD1SvlpudwG5zMHDPbe7gFySbCRp7cVvrJ8FOSUxMVExsSk6uKW0vL1u/bMm7nyqwmsdVmJiYp4OGIHcRpwH4rzCjRivcLtdcZ5kbqyXX+I8BgZRYKSnlXh7OpjyxxryHyeLc253AbnMweKS211ALrtdKYbeXnzWALcTcR6I8wo3YjxIty/OkwpXrMfAIAAAgJ1SjFSlGOa0AwAAgLzFjFgvv8R5DAwCAADYKVWGUnXrI4NmtAEAAABzmRHr5Zc4r3DMiwQAAAAAAABggxmDAAAAdkpVqsxIDjGnFQAAAJjJjFgvv8R5DAwCAADYKcUwlGLcenqIGW0AAADAXGbEevklziOVGAAAAAAAACiEmDEIAABgJzYfAQAAKLgK0+YjDAwCAADYKVWGUhgYBAAAKJDMiPXyS5xHKjEAAAAAAABQCDFjEAAAwE6kEgMAABRcpBIDAAAgS+xKDAAAUHCxKzEAAAAAAACAAo0ZgwAAAHZK/f9hRjsAAADIW8yI9fJLnMfAIAAAgJ1STNqV2Iw2AAAAYC4zYr38EueRSgwAAAAAAAAUQswYBAAAsFOKkXaY0Q4AAADyFjNivfwS5zEwCAAAYCfWGAQAACi4CtMag6QSAwAAAAAAAIUQMwYBAADslCqLUmQxpR0AAADkLWbEevklzmNgEAAAwE6pRtphRjsAAADIW8yI9fJLnEcqMQAAAAAAAFAIMWMQAADATikmpRKb0QYAAADMZUasl1/iPAYGAQAA7MTAIAAAQMFVmAYGSSUGAAAAAAAACiEGBgEAAOyUalhMO+yxdu1atWnTRiEhIbJYLPr+++8z1Pnnn3/Utm1b+fj4yMvLS3fffbciIyOt5xMSEvT8888rICBARYoUUdu2bXXkyJFbfUkAAAAKjNyI83ILA4MAAAB2Sk8vMeOwR3x8vKpXr64pU6Zken7//v269957VaFCBa1evVp//fWXXn31Vbm5uVnrDBgwQN99953mz5+v9evX68KFC2rdurVSUlJu6TUBAAAoKHIjzsstrDEIAACQT7Ro0UItWrTI8vyIESPUsmVLTZgwwVpWpkwZ6/+fO3dOM2bM0Oeff64mTZpIkr744guFhobql19+UfPmzXOu8wAAAMhzmDEIAABgpxQ5mHZIUlxcnM2RkJBgd59SU1O1ePFilStXTs2bN1dgYKDuuusum3TjLVu2KCkpSc2aNbOWhYSEqEqVKtqwYcMtvy4AAAAFgZlxXl6XP3oJAACQhxgmrTtj/H/tmdDQUPn4+FiP8ePH292n2NhYXbhwQW+++aYefPBBLV++XI888ogeffRRrVmzRpIUExMjFxcXFS1a1ObaoKAgxcTE3PoLAwAAUACYEeulx3l5HanEAAAAuSwqKkre3t7Wx66urna3kZqaKkl66KGH9OKLL0qSatSooQ0bNuijjz5SgwYNsrzWMAxZLPkjeAUAAIB5mDEIAABgJ7M3H/H29rY5bmZgMCAgQE5OTqpUqZJNecWKFa27EgcHBysxMVFnzpyxqRMbG6ugoKCbfDUAAAAKlsK0+QgDgwAAAHZKMRxMO8zi4uKiOnXqaM+ePTble/fuVenSpSVJtWrVkrOzs1asWGE9Hx0drZ07d6pevXqm9QUAACA/y2txXk4ilRgAACCfuHDhgv777z/r44MHD2r79u3y8/NTqVKlNGTIELVv317333+/GjVqpKVLl+rHH3/U6tWrJUk+Pj7q0aOHBg0aJH9/f/n5+Wnw4MGqWrWqdZdiAAAAFB4MDAIAANgpVRalmpB4kSrDrvqbN29Wo0aNrI8HDhwoSeratatmz56tRx55RB999JHGjx+v/v37q3z58vr222917733Wq+ZNGmSnJyc1K5dO126dEmNGzfW7Nmz5ejoeMvPBwAAoCAwI9azN87LLQwMAgAA2MmsdWPsbaNhw4YyjOsHmU8//bSefvrpLM+7ubnpgw8+0AcffGDXvQEAAAoLM2I91hgEAAAAAAAAkGcxYxAAAMBOZi0onXKD2X8AAAC4/cyI9fJLnMfAIAAAgJ3S1p259fQQM9oAAACAucyI9fJLnEcqMQAAAAAAAFAIMWMQAADATqlyUEou7EoMAACAnGdGrJdf4jwGBgEAAOzEGoMAAAAFV2FaY5BUYgAAAAAAAKAQYsYgAACAnVLloFRSiQEAAAokM2K9/BLnMTAIAABgpxTDohTj1neaM6MNAAAAmMuMWC+/xHmkEgMAAAAAAACFEDMGAQAA7JRi0q7EKfkkxQQAAKAwMSPWyy9xHgODAAAAdko1HJRqwq7EqflktzoAAIDCxIxYL7/EeaQSAwAAAAAAAIUQMwYBAADsRCoxAABAwUUqMQAAALKUKnN2mku99a4AAADAZGbEevklziOVGAAAAAAAACiEmDEIAABgp1Q5KNWE71fNaAMAAADmMiPWyy9xHgODAAAAdkoxHJRiwq7EZrQBAAAAc5kR6+WXOC9/9BIAAAAAAACAqZgxCAAAYKdUWZQqMzYfufU2AAAAYC4zYr38EucxMAjkYTt+L6KvpwZq3w4PnT7urFEzDqpei3PW881DamR6Xc9XjuqJvidsygxDeqVzGW1e5Z2hHeRvVe66oCeejVVE1YvyD07W6KfDtHGZb253C7eJf1CCnh5yWLXvPysXt1QdPeSmycPv0H+7PHO7awUaqcQAbtWPs/319bRAnY51Vulyl9VnzFFVvSs+y/qLZgVo0awAHT/iosCQRHV44biaPnHGps6Fc46a/WawfvvZV+fPOSo4NFHPjDqquo3P5/TTwVVadz2pJ/rEyi8wSYf3uumjUSW088+sP5er3n1BvUcdVelyl3XquLO+nhaoxZ8H2NS5t+VZdRkSreKlExV92EWz3yquDUt9rec7D4zWU4OO21xzOtZJHWtWsT4eNOmwmrWz/Zn5Z6uHBrQpdwvPFtnRqlO0Hu95TH6BiTq8z0PT3wjXrs3eWdavWveceg0/pNIRF3Uq1kXffFJCS+YFW8/Xa3ZK7fscUUjpy3JyMnT0sJsWzgjRyh8Cr7pnjFp1jFFQyQRJ0uF97po7JVSb1xbNuSdagBSmVGIGBoE87PJFB5WpfEnNOpzW6z3DM5yft32nzeNNK701aVCo7m2VcdDvu0+KyZI/vrCAndw8UnVgt7uWL/DTyE8P5XZ3cBt5eifr3fk79dcf3nq1Z0WdPeWskFKXFX+ej3cAyMtW/+Crj0aV0HPjjqhy3Xgt/jxArzxZRp+s/leBJZMy1P9xjr9mjS+uF96OUvkaF7Vnm4cmDwmVl0+K7m4WJ0lKSrRoeIey8g1I0isfH1JASKJOHHWRu2fK7X56hVqDtmfUZ/RRTXm5pHZtKqJWT53U2C8OqFfDCjpxzCVD/aDQBI39/IB+nuunt54vrcp14vXcuCM6d8pJ65f4SpIq1orXy9MOac7bxbXhZx/Va3FOIz46pIGPRGjPtiLWtg7966aXOpS1Pk5NyRj8b1rppXcHlrI+Tk7iD4Scdn/Lk+o94pA+HF1Gu7d6qWWH43r9093q3aKmTkS7ZqgfVPKyxnzyj5Z+FaS3B0eo0p3n1W/0AZ077azflvlLks6fddKCaSUVdcBdyUkOqtvotAa++Z/OnnLW1vVpA38nY1w0653SOnbYTZLU5JFYjZz2r557qLoi//O4fS8A8rz8MXyZh3Tr1k0PP/xwhvLVq1fLYrHo7NmzGc6VL19eLi4uOnr0qE3d6x2zZ8++br2YmJgs+/jtt9/qrrvuko+Pj7y8vFS5cmUNGjTIen727Nk2bRUvXlzt2rXTwYMHrXXCwsIyve+bb74pSTp06FCWffv999+t7SQmJmrChAmqXr26PDw8FBAQoPr162vWrFlKSkq66de0sKjzwHl1Gxaje1tmPrvPLzDZ5ti4zEfV619Q8dKJNvX273LTt9OLaeDEyNvRbdxmm1d5a86E4vrtZ9/c7gpusyeeOaoT0S6a9FKE9v7tpdijbtq+0VfRkW653bUCL0UOph3IO4jziPNul4UfF1PzjqfV4snTKhWRoGfHHFWxkCT99FlApvV//cZPLTufUsOHzqp46UQ1fPismnc8ra8+vDI7aNl8P50/66hRMw+qct14BZVMUpW74lW28uXb9bQg6dFeJ7Rsvp+WzvNX1H9u+mhUSZ045qzWXU5mWr/1U6cUe9RZH40qqaj/3LR0nr+WL/DTY31irXUe6XlCW9d6acGUIEXtd9OCKUHavt5Lj/S0zRBKSZHOnHC2HudOZ/yiMCnRYlPn/Fm+TMxpjzx9TMu/CdSyr4MUtT9ttuCJGFe16pT57/pWHWMUG+2q6W+EK2q/h5Z9HaTl3wbqsR5HrXV2/OmjDSv8FbXfQ9GRbvphTogO7imiyrWvzA7+Y6WfNq0pqqOH3HX0kLvmTCqtyxcdVaEGM4izI7fivKlTpyo8PFxubm6qVauW1q1bd936X375pfVzuHjx4urevbtOnTpl1z35LZDD1q9fr8uXL+uJJ57Q7NmzNWLECNWrV0/R0dHWOi+88ILi4uI0a9Ysa5mPj4/++OMPSdKePXvk7W07zTgwMFCZ+eWXX9ShQweNGzdObdu2lcVi0e7du/Xrr7/a1PP29taePXtkGIb+/fdf9e7dW23bttX27dvl6OgoSRozZox69eplc52Xl1eG+1WuXNmmzN8/7VuMxMRENW/eXH/99Zdef/111a9fX97e3vr999/1zjvvqGbNmqpRo8aNXkJk05kTTvrzV28NnnzYpvzyRYve7Bumfm8ckV9gci71DkBOuLvxaW1Z56uX39+jqnXP6dRxV/30ZbCWfhWU210r8FINi1INE9YYNKEN5B7iPOK8m5GUaNG+vz3U/rlYm/JaDc5r9+YiWV7j4pZqU+bqlqo92z2UnCQ5OUu/L/dRxVrxmvJySW1c5iMf/2Q1euSM2vWL1f/fduQwJ+dURVS7qAUf2v4b3rLGS5VqZ54mXrFWvLassf23t3m1l5p3OCVHJ0MpyRZVrBWvhZ8Us62zJuPAYInwRM3dslNJiQ76d5uHZr1ZXDGRtjPSqt1zQQv+2qkLcY7asbGIZr1VXOdOOd/sU8YNODmnKqLyBX09vYRN+db1vqp0Z+YDdBVqXtDW9b629df5qvnjsXJ0SlVK8rWDTYZq3HNOJcMvaebbpTNt08HB0H0tTsnNI0X/bvfKtA5smRHr2Xv9ggULNGDAAE2dOlX169fX9OnT1aJFC+3evVulSpXKUH/9+vXq0qWLJk2apDZt2ujo0aPq06ePevbsqe+++y7b92VgMIfNmDFDnTp1UoMGDdSvXz+9/PLLcnFxUXDwlfUB3N3dlZCQYFN2tcDAQPn6+mbrfj/99JPuvfdeDRkyxFpWrly5DN/UWiwW6/2KFy+uUaNGqXPnzvrvv/9Uvnx5SWnBYVZ9Sufv759lncmTJ2vt2rXavHmzatasaS0vU6aMnnjiCSUmJmZ6HW7Oiq/85O6ZkmF24fTRJVSpdrzqPRiXSz0DkFOCQy+rVacYLZwZogUflVC5ahfU59WDSkq06NfvMx9YAGAe4jzivJsRd9pRqSkW+QbYpgz7FkvSmdjM/2Cv1fC8ls71V70Hz+mOqpe07293LZvvp+QkB5077ST/oGRFH3bR9t889cAjZzT2iwM6esBVU0aUVEqyRZ0HHs+0XZjL2y9Fjk7S2ZO2A21nTzqraGDmg0BFA5Mzre/kLPn4Jet0rLOKFkvW2RPX1DmRVp7u321F9PYL7jpywFVFiyWrY/8YTfphn555oILOn0n7s3/zKm+t+8lXx4+4KLhUoroOidaEr/bruRbllJTIDPac4F00WY5O0pnMfiYCMv89WTQgUWdP+tqUnTnpLCdnQ95Fk3XmRFpKuodnsr5Yv1nOLoZSU6UPR5fRtt9srwsrF6+JX+2Qi2uqLl101Ot9K5BGnIdNnDhRPXr0UM+ePSWlfdYuW7ZM06ZN0/jx4zPU//333xUWFqb+/ftLksLDw9W7d29NmDDBrvsyMJiDzp8/r6+//lp//PGHKlSooPj4eK1evVqNGjXKsXsGBwdr7ty52rlzp6pUqXLjC/7P3d1dkqxpH2b48ssv1aRJE5tgMZ2zs7OcnW/tm6mEhAQlJCRYH8fFFe6Br2Xz/fTAI2fk4mZYyzYu89b237w0dfmeXOwZgJxisUj7dnpqzsS0b4f37/ZU6YiLatUphoHBHJZqUhpwKqnE+RZxHnHerbp27WfDsCirDSyfHBCjM7FOeqF1ORmGVLRYkpq2O62vpwZZZwMahuTrn6wX3o6So6MUUe2STh131jfTAhkYvM0Mw/axxWJIRuZ1M6ufXvnq8gx1rmlz86orM48P/Svt3uyh2Rv+UdMnTmvhx2kxwZpFVzadOLzHXfv+8tBnf+xW3cZxLEmTw4xrZo5ZLBnLbOvbPrb+vriq/FK8o/q1rS73Iqmqcc9Z9Rp+SNGRbtrxp4+1zpGD7urXtro8vVNUv/kpDZqwT0OfrMLgYDaYEeulx3nXfoa5urrK1dV2Nm9iYqK2bNmil156yaa8WbNm2rBhQ6bt16tXTyNGjNCSJUvUokULxcbG6ptvvlGrVq3s6ifR6E346aef5OnpaXO0aNEiQ7358+crIiJClStXlqOjozp06KAZM2bYfb+SJUva3Cv9m97MPP/886pTp46qVq2qsLAwdejQQTNnzrQJrK515MgRvf322ypZsqTKlbuyI9WwYcMyPM/Vq1fbXFuvXr0MdVJS0hY43rdvnypUqJCt55jd1/Rq48ePl4+Pj/UIDQ3N1r0Koh1/FNGR/W56sJPtWgLbf/NS9CEXPVqhqlqEVleL0OqSpNd7hWnIY3fkRlcBmOj0CWdF/uduUxa130PFijNTJ6elGg6mHchbiPOuIM7LGd5+KXJwNHTmmhlg50462cwAu5qru6FBk6K0aP9f+uyP3fp8024FhSbKwzNF3n5p1/gFJqtEmQSbtOFSEZd1OtZZSYksW3A7xJ12VEpy2sDt1Xz8k3XmROZzcs7EOmWo7xuQrOQkKe7/M/3OnHBS0cCMdc6czHqeT8IlRx36100lwrP+/XA61lmxR52vWwe3Ju6Mk1KSJb9itrGZj3+SzmaRwn3mpEvGnwn/JCUnWRR31ZqQhmFRdKS7DvxTRAtnltD6pf5q3+eozXXJSQ6KjnTXvp2emv1uaR34p4ge6hot3JiZcV5oaKjNZ1pms/9OnjyplJQUBQXZLgkUFBSU5drD9erV05dffqn27dtbMxZ8fX31wQcf2PVcmTF4Exo1aqRp06bZlP3xxx/q3LmzTdmMGTNsyjp37qz7779fZ8+ezXbKiCStW7fOZs0XJ6es37YiRYpo8eLF2r9/v1atWqXff/9dgwYN0nvvvaeNGzfKwyPtm4Fz587J09NThmHo4sWLuvPOO7Vw4UK5uFzZKWvIkCHq1q2bTfslStiujbBgwQJVrFjRpix97RrDMGTJ5ja42X1NrzZ8+HANHDjQ+jguLq5ABY32WDbPXxHVLmZYXLr9c8fV4prBwt4PVFDv0UetO9gByL92b/VWyfBLNmUlwi4p9ljGHe4AZA9x3hXEeTnD2cVQRLWL2rrWS/VbXFkCZutaL93TPPMN59I5OUvFQtIGDNb8UFR1m8TJ4f/fL1SqE6/V3xVVaqqsZUcOuMovKEnOLteZrgbTJCc5aN/fHrrz/vPasNTXWn7n/ee1cZlPptf8s6WI7mpq+77XanBee//2UEqyxVrnzvvO67tPrmQD1Lo/6zUpJcnZJVWhEQna+YdnlnW8iiarWPEknY5ljcGckpzkoH27PFWz/lltWOFvLb+z/llt/MUv02v+3eapux44Y1N2571ntW9nkUzWF7zCYkl7368nO3VgvqioKJv1hK+dLXi1az9br/d5u3v3bvXv318jR45U8+bNFR0drSFDhqhPnz52fVnJwOBNKFKkiO64w3a21ZEjR2we7969W3/88Yc2bdqkYcOGWctTUlI0b948Pfvss9m+X3h4uF0BpiSVLVtWZcuWVc+ePTVixAiVK1dOCxYsUPfu3SWlrSuzdetWOTg4KCgoSEWKZPxQCQgIyPA8rxUaGpplnXLlyumff/7JVn+z85peK7PptwXNpXgHHTt45TnGRLlo/053efkmK7BkWlAYf95Ba3/00TOjjmW4Pn234msFlkhScClmFBUUbh4pCrnqm97gUokqU/mizp9x0oljLte5Evnd97OK690FO9W+zxGtXeKv8tUvqEX743r/1bK53bUCL0UWpWSV82dnO8hbiPOuIM7LOY8+c0Jv9y+lctUuqmLteC35wl+xR53V6v87184cV1wnY5w19P1ISdKR/a7as91DFWrG6/w5Jy2cXkyH9rhp8HuR1jZbdzmpRTMDNO3VEnro6ZM6etBV898P0kM9Mt8NFzlj4SfFNOS9SO39y0P/bCmilp1PKbBEkhZ/nrbjdPeXjimgeJLefiFtGZCfPvdX2+4n9cyoo/r5S39VrBWv5h1O681+VzaR+H5GMb3z7T6163tcG5f56J7m51TzvvMa+EiEtU6vV4/q9xU+ij3qLN+AZHV64bg8PFO04uu0wSc3jxQ9NShG65f46vRxJwWFJqr7S9E6d8ZJv/2c+aAlzPHdzBANfnuf9u301D/bvNSi/XEVK56gJfPSZoZ1G3RY/kGJendo2vu5eF6w2nSOUa/hB7X0qyBVrHlezR6P1VsDr8z6btf7iPbt9FR0pJucnFNVp8FZNX74hKaMKmOt03XgYW1eW1Qnol3kUSRFDVqdVNW7zunVHpVu7wuQT5kR66Vf7+3tnWGjsWsFBATI0dExw+zA2NjYDLMI040fP17169e3rj1crVo1FSlSRPfdd5/Gjh2r4sWLZ6ufDAzmkBkzZuj+++/Xhx9+aFP++eefa8aMGXYFjLcqLCxMHh4eio+/shOWg4PDDYPBW9WpUye9/PLL2rZtW4b1Z5KTk5WQkJBpoIor9v7loaGPX3mfpo9O+ya/abvTGjw5LRBc80NRybCo0cNnMm0DBV+56hf19jf7rY/7jE4bJF7+VVG9+2LmO5OhYNi7w0uv9yuvboMi1em5KMUccdP0N8K1alGxG1+MW2JWGjCpxPkTcR5x3q1q+NBZnT/jqC8nBet0rJNKl7+ssV8cUND/v/g9HeusE0evfLmXmip9+1ExHdkfKkdnQ9XrXdCkH/YpOPTKF72BJZI0bt5+TR9dQn2alFdAcJIe7nlC7frFZrg/cs6aRUXlVTRFT74YI7/AZB3e46ZXniqj2P+/n35BSSoWcuV9Ox7lqleeKqPeo4+qTdeTOn3cWdNGltD6Jb7WOrs3F9G4vmHqNjRaXYbEKPqwi8Y9G6Y92678GwsonqThHx6St1+Kzp1y0r9bPTSgTTnrfVNTLQqrcFlNHj+oIt4pOh3rpL82eGrcs2G6FM+21Tlp7ZIAefkmqVO/I/ILTNShvR4a2auiYo+5SZL8AhMVGHLlS/7jR9w0sldFPfPyIbXpHKNTx1300dhw/bbsyoxDN49U9Rt9QAHBiUq87KCoA+56e3CE1i4JsNYpGpCkIW/vk19gouLPO+rgv0X0ao9KGTYoQebMiPXsud7FxUW1atXSihUr9Mgjj1jLV6xYoYceeijTay5evJgh0+Dqmf3ZxcBgDkhKStLnn3+uMWPGZFgYumfPnpowYYL++usvVa9ePVvtxcbG6vJl2xRRf3//TBd1Hj16tC5evKiWLVuqdOnSOnv2rN5//30lJSWpadOmdj2P8+fPZxit9vDwsBnpPnXqVIY6vr6+cnNz04ABA7R48WI1btxYr7/+uu699155eXlp8+bNeuuttzRjxgzVqFHDrj4VNtXrXdCyY9uvW6dl51Nq2fnUdetc7UbtIf/5e6OXmpeokdvdQC75c5Wf/lyVeSoKAPMR5xHnmaVNt1Nq0y3zGC79C+B0pSISNHXF3hu2Wan2Rb330z5T+oeb99OcAP00JyDTc5l9abvjd08992DW64tK0vrFvlq/2DfL8+P7hl33+sTLDhrxJBkFuWXx3OJaPDfz2VsTh0VkKNvxp4+efzjrz5HPJpXSZ5NKXfeek19mTfn8ZuDAgXrqqadUu3Zt3XPPPfr4448VGRmpPn36SEpbZuPo0aP67LPPJElt2rRRr169NG3aNGsq8YABA1S3bl2FhIRk+758TZ0DFi1apFOnTtmM8qaLiIhQ1apV7cr3Ll++vIoXL25zbNmyJdO6DRo00IEDB9SlSxdVqFBBLVq0UExMjJYvX37dxawzM3LkyAz3HTp0qE2dJk2aZKjz/fffS0pLAVmxYoWGDh2q6dOn6+6771adOnX0/vvvq3///nbtpgcAQF6SoispJrd2IL8hzvteEnEeAKBgMyfWs0/79u01efJkjRkzRjVq1NDatWu1ZMkSlS6d9oVCdHS0IiOvfHHUrVs3TZw4UVOmTFGVKlX0xBNPqHz58lq4cKFd97UY9swvBPKwuLg4+fj46MzeMvL2Ysy7MGpeouaNK6FAc/j/wvsofJKNRK2Mn6dz587dcA2XW5H+WfPK783k5nnri7VfvpCksXcvz/F+A/kdcR6I8wo3YrzC7XbFeZK5sV5+ifP4VAUAAMgn1q5dqzZt2igkJEQWi8U6eyszvXv3lsVi0eTJk23KExIS9PzzzysgIEBFihRR27Ztb7gRBAAAAAomBgYBAADslGI4mHbYIz4+XtWrV9eUKVOuW+/777/XH3/8ken6MgMGDNB3332n+fPna/369bpw4YJat26tlBQSmwEAACTzYr38gM1HAAAA7GTIolRZTGnHHi1atFCLFi2uW+fo0aN67rnntGzZMrVq1crm3Llz5zRjxgx9/vnnatKkiSTpiy+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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_7_t_1_trn_250>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_7__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_7__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:18<00:00,  1.91it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6632124352331606,\n",
      "                 'precision': 0.5289256198347108,\n",
      "                 'recall': 0.8888888888888888,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.6532663316582914,\n",
      "                       'precision': 0.9154929577464789,\n",
      "                       'recall': 0.5078125,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.5294117647058822,\n",
      "                  'precision': 0.4909090909090909,\n",
      "                  'recall': 0.574468085106383,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.631578947368421,\n",
      " 'macro avg': {'f1-score': 0.6152968438657781,\n",
      "               'precision': 0.6451092228300935,\n",
      "               'recall': 0.6570564913317573,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.6325981325110337,\n",
      "                  'precision': 0.722018099159416,\n",
      "                  'recall': 0.631578947368421,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_7_t_1_trn_250>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>▃▁▁▄▁▁▁▄▂█▁▁▄▁▁▁</td></tr><tr><td>train_mean_token_accuracy</td><td>▄██▄███▅▄▁██▄███</td></tr><tr><td>valid_loss</td><td>▁▁▄▃▆▂▄▃▃██▄▃▅▁▃</td></tr><tr><td>valid_mean_token_accuracy</td><td>██▆▇▃▇▃▆▆▁▄▆▆▆█▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.63158</td></tr><tr><td>f1</td><td>0.63158</td></tr><tr><td>precision</td><td>0.63158</td></tr><tr><td>recall</td><td>0.63158</td></tr><tr><td>train_loss</td><td>1e-05</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.09318</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.97744</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_gpt_zero</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/83bd2oxn</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_121444-83bd2oxn\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chatGPT",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
